Integrating Large Language Models into Automated Machine Learning: A Human-Centric Approach

The growing complexity and volume of data in modern applications have amplified the need for efficient and accessible machine learning (ML) solutions. Automated Machine Learning (AutoML) addresses this challenge by automating key stages of the ML pipeline, such as data preprocessing, model selection and hyperparameter tuning. However, AutoML systems often remain limited in their ability to interpret user intent or adapt flexibly to domain-specific requirements. Recent advances in Large Language Models (LLMs), such as GPT-based models, offer a novel opportunity to enhance AutoML through natural language understanding and generation capabilities. This paper proposes a software system that integrates LLMs into AutoML workflows, enabling users to interact with ML pipelines through natural language prompts. The system leverages LLMs to translate textual descriptions into code, suggest model configurations and interpret ML tasks in a humancentric manner. Experimental evaluation across diverse public datasets demonstrates the system’s ability to streamline model development while maintaining high performance and reproducibility. By bridging the gap between domain expertise and technical implementation, this integration fosters more intuitive, scalable and democratized ML development. The results highlight the potential of LLMs to transform AutoML into a truly interactive and accessible tool for a broader range of users.